Title: Quantum Error Correction for Graph-Based Algorithms

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum error correction is a critical challenge in the development of quantum computing, particularly for applications involving complex data structures like graphs. Classical error correction techniques, while effective, are often limited by the inherent complexity of the data and the need for precise calibration. This paper explores a novel quantum error correction scheme specifically designed for graph-based algorithms, aiming to enhance resilience against noise. We propose a hybrid quantum-classical approach that leverages quantum computations for error detection and correction within a classical graph representation. This design offers a generalizable solution, potentially surpassing existing techniques in terms of applicability and robustness. The proposed scheme focuses on dynamically adapting to the graph's structure, mitigating the limitations of fixed-parameter error correction. This work details the theoretical underpinnings and preliminary experimental validation of the proposed approach.

DOI: 10.5281/zenodo.22314466

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